Hand Motion Analysis of Different Segments of a Procedure: Is One Segment Enough?

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Jeffrey Weinstein, Hamza Ali, Oussama Metrouh, Ammar Sarwar, John D. Mitchell, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5054500/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Purpose This study aims to examine if the hand motions of operators associated with certain parts of central venous access are more important than others in distinguishing between experts and non-experts. Materials and methods Experts (n = 10) and Trainees (PGY2; n = 18) performed central venous access on a phantom 4 times each as their needle hand and ultrasound probe motions were tracked. Path length-time graphs were used to divide the procedure into three phases: (1) the access phase: visualizing the internal jugular vein on ultrasound and needle placement; (2) the wire phase: passing a wire through the needle; and (3) the confirmation phase: confirming the intravascular wire position and threading a dilator on the wire. Comparisons between trainees and experts were made for the complete trial, and each phase using Mann-Whitney U tests with Benjamini-Hochberg correction. Receiver Operating Characteristic analysis was performed to compare the performance of each phase in differentiating between experts and trainees. Results Motion data from 10 experts and 18 trainees was analyzed. Experts and trainees differed significantly for all the motion metrics (p<0.001). A comparison of the phases showed that the access phase (AUC = 0.96; R2 = 0.79) and the wire phase (AUC = 0.95; R2 = 0.59) were able to distinguish between experts and trainees with an accuracy comparable to the complete trial (AUC = 0.94; R2 = 0.69). Conclusions The access phase of simulated central venous access can best differentiate between experts and trainees. This sample of hand motion performance may be able to simplify motion analysis of technical performance and obviate the need for recording hand motion for the entire procedure. Hand motion analysis procedure segmentation technical performance simulation Figures Figure 1 Figure 2 Figure 3 Introduction Hand motion analysis has emerged as a tool for objective analysis of technical performance in procedural medicine [ 1 – 10 ]. Most of these studies are performed in the simulation environment and assess a discrete task where motion and video recordings produce manageable amounts of data for analysis. While components of procedures can easily be analyzed using electromagnetic tracking equipment for various metrics such as path length (the total distance the operator’s hands traveled), translational movements (suprathreshold movements made in the horizontal and vertical plane), rotational sum (total degrees of rotation), rotational movements (suprathreshold rotatory movements), and time, entire, more complex, procedures prove to be more challenging to analyze. Multi-hour cases have the potential to produce tremendously large datasets and may limit the ability to translate the use of this technology into patient procedures. Additionally, live patient procedures have additional factors not present in the simulation environment, such as different instrument setups, patient motion, and variant anatomy, which can contribute to additional or unpredictable hand motions that are not directly related to the procedure (e.g., reaching over for an instrument on the other side of a table). This makes the comparison of hand motion profiles in the clinical setting for objective analysis of technical proficiency challenging. Hand motion analysis of a critical portion of a procedure may stand as a surrogate for the overall performance of a procedure. Such a portion would ideally be least affected by factors not relevant to the procedure and will vary across various types of procedures. Practically speaking, the task of dividing a procedure into ‘phases’ can be done using visual data (video) [ 10 – 12 ]; however, this is time consuming, requires individual review, and is potentially difficult to automate. Evaluation of graphical motion data for consistent motion events has the potential to allow for automated identification of phases and analysis of areas of interest. The purpose of this study is to evaluate if hand motion analysis of a given phase of a procedure can accurately differentiate between expert and novice operators as compared to analysis of the entire procedure. Being able to analyze a phase or portion of a procedure will streamline hand motion evaluation in longer, more complex simulation procedures and allow for its incorporation into real patient procedures. Methods and Material Participants This study received institutional review board (IRB) approval with a waiver of documentation of informed consent by the Committee on Clinical Investigations (CCI). Verbal informed consent was obtained from all participants, including 10 experts (6 Interventional Radiologists and 4 Anesthesiologists) and 18 postgraduate-year 2 trainees (2 Interventional Radiology and 16 Anesthesiology trainees). Motion data capture and analysis Central venous access on a standardized manikin (SimuLab CentraLine System; SimuLab Corporation, Seattle, WA) was used as a model procedure to demonstrate segmentation. The trainees practice on this manikin as a part of their regular training. Participants were allowed 1–2 practice trials on the manikin before recording trials. For this analysis, trainee motion only up to a maximum of 5 trials was included to limit the effect of “learning” the simulator. If the participant was unable to secure access in the vein without reinserting the needle or did not complete all the outlined steps, the trial was considered to be a failed attempt. Video recordings were used to correlate motion data with the steps of the procedure [ 13 ]. The data collection and analysis protocols were consistent with previously published work [ 10 , 14 , 15 ]. The procedure steps were standardized according to prior work [ 10 ], and motion data were captured using commercially available electromagnetic motion tracking technology (Polhemus Liberty; Polhemus, Colchester, VT). Each attempt at the procedure was labeled a trial. Motion sensors (Teardrop Mini; Polhemus, Colchester, VT) were attached to the base of the ultrasound probe (Butterfly iQ+; Butterfly Network, Inc., Guilford, CT) and dorsum of the needle hand. Motion metrics (path length, rotational sum, translational movements, rotational movements, and time) were calculated from the motion data. Path length refers to the total distance, in cm, and the rotational sum is the total rotation, in degrees, covered by the hand/probe over the course of the trial. Translational and rotational movements were defined as changes in velocity greater than an empirically defined threshold (translational velocity = 0.05cm/s; rotational velocity = 0.05°/s) followed immediately (in the next frame, 1/240th of a second later) by deceleration [ 14 ]. Phases of the procedure Reliably predictable events from the motion data (transition points) were used to segment each trial into phases. The first phase, the "access phase," consisted of identifying the internal jugular vein (IJV) and carotid artery and securing venous access. The first transition point was the motion associated with placing the probe down after successfully gaining venous access. The second phase, known as the "wire phase," involved threading the guidewire through the needle. In this phase, only needle hand motion was considered as the probe was stationary. The second transition point was the motion associated with picking up the probe again for the final phase, the "confirmation phase", which consisted of confirming the intravascular position of the wire with the ultrasound in both the transverse and longitudinal planes; the trial concluded when the dilator touched the 'skin' of the manikin. Segmentation of the trials was achieved with the aid of path length-time graphs, an example of which is illustrated in Fig. 1 . Motion and Statistical Analysis Motion analysis was performed using an in-house algorithm developed in R 4.1.2 (R Foundation for Statistical Computing, Vienna, Austria). Motion metrics for the needle hand and probe were summed for the analysis. Normality of the path length, rotational sum, translational movements, rotational movements, and time data was assessed via the Shapiro-Wilk Test and found to be non-normally distributed. Therefore, Mann-Whitney U tests were used to compare the motion metrics between experts and trainees. Motion metrics are reported as median ± interquartile range. A significance threshold of 0.05 was set, and the false discovery rate was controlled using the Benjamini-Hochberg correction. The ability of each phase to predict an expert operator was evaluated using the receiver operating characteristic (ROC) curve analysis. The Bland-Altman method was used to compare the observed time-to-event on the video to the predicted time-to-event from the motion data. Results Motion data were recorded for 41 trials from experts and 83 trials from trainees with a mean number of 4 trials per participant. All trials where participants did not fully execute the standardized steps (n = 11) were excluded from the analysis. Therefore, all 41 trials for experts and 72 trials from trainees were included in the analysis. Experts had significantly lower motion metrics (where lower is better) than trainees for the complete trial (p < 0.001; Table 1 ). The Bland-Altman plot showed comparable measurements of time-to-event from motion data and video recordings (mean difference = 0.08s; 95% confidence interval = (-1.55s) – (+ 1.72s)) (Fig. 2 ). When the data was analyzed by phases, significant differences were still observed between the cohorts for all metrics in the first two phases (except path length in the access phase) (Table 1 ). These findings were supported by the area-under-the-curve (AUC) from the ROC analysis and the fit of the models (Fig. 3 ), which was similar for the complete trial and the first two phases (AUC: complete trial = 0.94; access phase = 0.96; wire phase = 0.95). Conversely, the performance of the confirmation phase showed relatively less disparity between the two cohorts, as evidenced by the motion metrics and the model parameters results. Table 1 A comparison of motion metrics between trainees and experts across different phases of central venous access Motion metric Trainees (n = 72 trials) Experts (n = 41 trials) P value Full Trial Path length (cm) 1102.7 ± 320.7 863.2 ± 322.5 < 0.001* Rotational Sum 5055 ± 3298.4 2936 ± 1886.6 < 0.001* Translational movements 119.5 ± 41 81 ± 21 < 0.001* Rotational Movements 245 ± 100.2 141 ± 50 < 0.001* Time (s) 79.7 ± 53.7 42.2 ± 16 < 0.001* Access Phase Path length (cm) 222.9 ± 102.5 200.8 ± 107.5 0.38 Rotational Sum 1639.2 ± 1004 678.4 ± 958.1 < 0.001* Translational movements 28.5 ± 14.2 22.0 ± 15.0 0.04* Rotational Movements 85.5 ± 52.2 46.0 ± 26.0 < 0.001* Time (s) 21.7 ± 14.5 12.4 ± 4.4 < 0.001* Wire Phase (needle hand only) Path length (cm) 374.2 ± 123 245.5 ± 136.1 < 0.001* Rotational Sum 1290.8 ± 1053.1 655.7 ± 198.9 < 0.001* Translational movements 39.5 ± 10.8 26.0 ± 12.0 < 0.001* Rotational Movements 63.5 ± 31.2 37.0 ± 11.0 < 0.001* Time (s) 27.8 ± 16.7 14 .0 ± 5.2 < 0.001* Confirmation Phase Path length (cm) 311.2 ± 177 366.1 ± 160.7 0.34 Rotational Sum 2002.6 ± 1276.9 1243.7 ± 1005.3 0.002* Translational movements 33.5 ± 18.0 31.0 ± 18.0 0.34 Rotational Movements 65.5 ± 35.8 58.0 ± 26.0 0.03* Time (s) 20.2 ± 10 19.5 ± 6.2 0.12 *Statistically significant result; p values are corrected by Benjamini-Hochberg Method Discussion While all three phases could be used to distinguish between novice and expert operators, the ROC analysis showed that only the access phase or wire phase can be effectively used as a surrogate for the performance of the entire procedure. This may be because these phases require the most dexterity and coordination, one involving simultaneous ultrasound and needle coordination and the other requiring general dexterity with wire manipulation. Given that the access phase is performed first, it is likely the easiest to evaluate on a live patient and was the most valuable single segment in this simulation study. Future evaluation of the ability to use the access phase to differentiate novices from experts in the clinical setting is needed to validate this finding. Deconstructing tasks and analyzing key segments of procedures to assist in teaching have been investigated for open surgical [ 11 , 12 , 16 ], laparoscopic [ 17 ], and ultrasound-guided tasks [ 10 ]. This technique, in combination with motion metrics, can provide individualized feedback to trainees and potentially allow them to train components selectively. In the current study, gaining venous access was a key step that differentiated experts from non-experts, indicating that practice directed towards mastering this phase would be of the highest yield in training. This is supported by earlier studies examining the learning curve of ultrasound-guided central venous line placement [ 10 ]. Similarly, identifying segments that are not accurate indications of performance may consolidate data analysis. Using a segment that is least affected by factors not relevant to the procedure also helps reduce the effect on hand motion analysis from differences in setup. For example, if only the wire phase (which involves threading the wire through the needle) is used to assess technical skill, there is no need for a fixed starting position for the operator’s hands because the analysis starts only when access has been secured. For the access phase, although starting conditions may need to be standardized, the moment access has been secured, the probe then does not have to be placed in a standardized location. The ability of graphical data to be used to segment a procedure, and its high degree of correlation with video data, represents an advancement toward the practical incorporation of hand motion analysis in multistep procedures. Placing the ultrasound probe down or picking it back up produced a consistent motion signature that could be used to define the beginning or end of a phase. Similar motion signatures are likely to be found in other interventional procedures and may obviate the need for video recording for segmenting trials, which takes more time and increases the risk of breach of confidentiality for both trainees and (if done in the clinical setting) patients. Additionally, this may allow for computer-based automation of the process, further easing the transition into clinical use. This study has several limitations. The small sample size is a potential limitation; however, it did not limit the ability to detect differences between less and more experienced operators. The simulated nature of the procedure also represents a highly controlled environment with a set starting position for all the equipment, which may artificially reduce the variability of the motion recordings. A simulated environment lacks the fidelity of a live patient procedure where other elements, such as patient motion, may alter the results. Naturally, the segmentation of this central venous access simulation and the highest-yield segment for analysis cannot be directly translated to other procedures and would need to be revalidated in other procedures. This study demonstrates the proof-of-concept of using hand motion analysis to not only compare operators but to reliably segment a procedure and identify a segment that could accurately represent the operator’s overall performance. This could eliminate the future need for video recording trials for evaluation and eliminate the need for recording motion data for all the steps in a simulated central venous access procedure. Applying these concepts to even longer, more complex procedures are warranted to extend the utility of hand motion analysis into real-world practice. Statements and Declarations Acknowledgments This study was presented as an abstract at the Society of Interventional Radiology Annual Meeting 2023. The traditional poster received the Best Original Scientific Research Poster Award. Funding Information: This work was supported by CRICO Competing Interests: The authors have no relevant financial or non-financial interests to disclose. Ethical approval: This study was approved by the Institutional Review Board (IRB) at Beth Israel Deaconess Medical Center Informed consent Verbal consent was obtained from all participants. References V. Datta, A. Chang, S. Mackay, A. Darzi, The relationship between motion analysis and surgical technical assessments., American Journal of Surgery 184 (2002) 70–73. https://doi.org/10.1016/s0002-9610(02)00891-7. M.A. Hayter, Z. Friedman, M.D. Bould, J.G. Hanlon, R. Katznelson, B. Borges, V.N. Naik, Validation of the Imperial College Surgical Assessment Device (ICSAD) for labour epidural placement, Can J Anesth/J Can Anesth 56 (2009) 419–426. https://doi.org/10.1007/s12630-009-9090-1. K.J. Chin, C. Tse, V. Chan, J.S. Tan, C.M. Lupu, M. Hayter, Hand motion analysis using the imperial college surgical assessment device: validation of a novel and objective performance measure in ultrasound-guided peripheral nerve blockade., Regional Anesthesia and Pain Medicine 36 (2011) 213–219. https://doi.org/10.1097/AAP.0b013e31820d4305. R. Matyal, J.D. Mitchell, P.E. Hess, B. Chaudary, R. Bose, J.S. Jainandunsing, V. Wong, F. 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Wiggers, J.A. van der Hage, Global versus task-specific postoperative feedback in surgical procedure learning, Surgery 170 (2021) 81–87. https://doi.org/10.1016/j.surg.2020.12.038. M.A. Farcas, M.O. Trudeau, A. Nasr, J.T. Gerstle, B. Carrillo, G. Azzie, Analysis of motion in laparoscopy: the deconstruction of an intra-corporeal suturing task., Surgical Endoscopy 31 (2017) 3130–3139. https://doi.org/10.1007/s00464-016-5337-4. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5054500","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":437065829,"identity":"091ea674-b350-4361-9464-2368a9347527","order_by":0,"name":"Jeffrey Weinstein","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIiWNgGAWjYBACxnYgkQDE/BKMDRJQNhCwAfEBHFqaIcokJGcQq4WBGUJJGNwAEkRpYW7mMXzwcEddnfHt5sZbN2oY8vhnnzH8XFHGIMd3IwGHw3iMDRLPHJYwu3Ow2TrnGEOxxLkcY8kz5xiMJXFq4d0mkdh2QMLsRmKbdG4DQ2LDGbYEycY2hsQNuLVs/5HYVidhPAOqZf4ZtuSfQC31eLRsY0hsY5YwkIBq2XCG+RjIlgQDnFr4PwMVH5acAfGLROJGoBbLhnMShjPPPMCqxbC9LfHjz7Y6fv7Z7Q9v59TYJM47w9h8s6HMRp7vOHZbDBtQ+RIYDAwgj1NmFIyCUTAKRgEMAACRR2LIJfCIwQAAAABJRU5ErkJggg==","orcid":"","institution":"Beth Israel Deaconess Medical Center, Harvard Medical School","correspondingAuthor":true,"prefix":"","firstName":"Jeffrey","middleName":"","lastName":"Weinstein","suffix":""},{"id":437065830,"identity":"f6d70608-f55a-4bc9-a1f8-4258c54acef5","order_by":1,"name":"Hamza Ali","email":"","orcid":"","institution":"Beth Israel Deaconess Medical Center, Harvard Medical School","correspondingAuthor":false,"prefix":"","firstName":"Hamza","middleName":"","lastName":"Ali","suffix":""},{"id":437065831,"identity":"f228cf69-c388-4908-ad3e-963d8209450b","order_by":2,"name":"Oussama Metrouh","email":"","orcid":"","institution":"Beth Israel Deaconess Medical Center, Harvard Medical School","correspondingAuthor":false,"prefix":"","firstName":"Oussama","middleName":"","lastName":"Metrouh","suffix":""},{"id":437065832,"identity":"c4ae4af3-f400-4eec-bc47-7fc9d6b68daf","order_by":3,"name":"Ammar Sarwar","email":"","orcid":"","institution":"Beth Israel Deaconess Medical Center, Harvard Medical School","correspondingAuthor":false,"prefix":"","firstName":"Ammar","middleName":"","lastName":"Sarwar","suffix":""},{"id":437065833,"identity":"2319dda7-5ea0-4878-a831-263ee102cbfa","order_by":4,"name":"John D. 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The procedure can be divided into three phases based on when the probe is placed down and picked up again. The access phase represents the act of securing access in the internal jugular vein using the needle. The wire phase consists of threading the guidewire through the needle, and the confirmation phase consists of confirming the intravascular position of the wire.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5054500/v1/1dd446ac77565a6c1905dc5c.png"},{"id":83480465,"identity":"ee391394-7c87-409d-8d1d-7f9dfa9edee3","added_by":"auto","created_at":"2025-05-27 06:38:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":80375,"visible":true,"origin":"","legend":"\u003cp\u003eThe Bland-Altman Plot compares time-to-event measurement using video recording and motion data. The measured events are placing the probe down after securing access and picking up the probe again to confirm the intravascular position of the wire. One outlier (with a difference of 9.9s) was excluded from the chart for clarity.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5054500/v1/e55890de62ad253463ac0944.png"},{"id":83480463,"identity":"c2192e09-a28f-4536-8900-5b75dc31a168","added_by":"auto","created_at":"2025-05-27 06:38:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":55625,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic analysis. The complete trial, access phase, and wire phase had comparable ability to differentiate between experts and trainees (non-experts) in simulated central venous access.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5054500/v1/19c6ce1be91cabd148207d6e.png"},{"id":83480520,"identity":"35851b5b-ed64-4d5d-9dbc-84636687709b","added_by":"auto","created_at":"2025-05-27 06:38:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":697039,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5054500/v1/9c1a2f4d-d7fd-4be1-8693-c87982a2eade.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hand Motion Analysis of Different Segments of a Procedure: Is One Segment Enough?","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHand motion analysis has emerged as a tool for objective analysis of technical performance in procedural medicine [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8 CR9\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Most of these studies are performed in the simulation environment and assess a discrete task where motion and video recordings produce manageable amounts of data for analysis. While components of procedures can easily be analyzed using electromagnetic tracking equipment for various metrics such as path length (the total distance the operator\u0026rsquo;s hands traveled), translational movements (suprathreshold movements made in the horizontal and vertical plane), rotational sum (total degrees of rotation), rotational movements (suprathreshold rotatory movements), and time, entire, more complex, procedures prove to be more challenging to analyze.\u003c/p\u003e \u003cp\u003eMulti-hour cases have the potential to produce tremendously large datasets and may limit the ability to translate the use of this technology into patient procedures. Additionally, live patient procedures have additional factors not present in the simulation environment, such as different instrument setups, patient motion, and variant anatomy, which can contribute to additional or unpredictable hand motions that are not directly related to the procedure (e.g., reaching over for an instrument on the other side of a table). This makes the comparison of hand motion profiles in the clinical setting for objective analysis of technical proficiency challenging.\u003c/p\u003e \u003cp\u003eHand motion analysis of a critical portion of a procedure may stand as a surrogate for the overall performance of a procedure. Such a portion would ideally be least affected by factors not relevant to the procedure and will vary across various types of procedures. Practically speaking, the task of dividing a procedure into \u0026lsquo;phases\u0026rsquo; can be done using visual data (video) [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]; however, this is time consuming, requires individual review, and is potentially difficult to automate. Evaluation of graphical motion data for consistent motion events has the potential to allow for automated identification of phases and analysis of areas of interest.\u003c/p\u003e \u003cp\u003eThe purpose of this study is to evaluate if hand motion analysis of a given phase of a procedure can accurately differentiate between expert and novice operators as compared to analysis of the entire procedure. Being able to analyze a phase or portion of a procedure will streamline hand motion evaluation in longer, more complex simulation procedures and allow for its incorporation into real patient procedures.\u003c/p\u003e"},{"header":"Methods and Material","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThis study received institutional review board (IRB) approval with a waiver of documentation of informed consent by the Committee on Clinical Investigations (CCI). Verbal informed consent was obtained from all participants, including 10 experts (6 Interventional Radiologists and 4 Anesthesiologists) and 18 postgraduate-year 2 trainees (2 Interventional Radiology and 16 Anesthesiology trainees).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMotion data capture and analysis\u003c/h2\u003e \u003cp\u003eCentral venous access on a standardized manikin (SimuLab CentraLine System; SimuLab Corporation, Seattle, WA) was used as a model procedure to demonstrate segmentation. The trainees practice on this manikin as a part of their regular training. Participants were allowed 1\u0026ndash;2 practice trials on the manikin before recording trials. For this analysis, trainee motion only up to a maximum of 5 trials was included to limit the effect of \u0026ldquo;learning\u0026rdquo; the simulator. If the participant was unable to secure access in the vein without reinserting the needle or did not complete all the outlined steps, the trial was considered to be a failed attempt. Video recordings were used to correlate motion data with the steps of the procedure [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe data collection and analysis protocols were consistent with previously published work [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The procedure steps were standardized according to prior work [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and motion data were captured using commercially available electromagnetic motion tracking technology (Polhemus Liberty; Polhemus, Colchester, VT). Each attempt at the procedure was labeled a trial. Motion sensors (Teardrop Mini; Polhemus, Colchester, VT) were attached to the base of the ultrasound probe (Butterfly iQ+; Butterfly Network, Inc., Guilford, CT) and dorsum of the needle hand.\u003c/p\u003e \u003cp\u003eMotion metrics (path length, rotational sum, translational movements, rotational movements, and time) were calculated from the motion data. Path length refers to the total distance, in cm, and the rotational sum is the total rotation, in degrees, covered by the hand/probe over the course of the trial. Translational and rotational movements were defined as changes in velocity greater than an empirically defined threshold (translational velocity\u0026thinsp;=\u0026thinsp;0.05cm/s; rotational velocity\u0026thinsp;=\u0026thinsp;0.05\u0026deg;/s) followed immediately (in the next frame, 1/240th of a second later) by deceleration [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePhases of the procedure\u003c/h2\u003e \u003cp\u003eReliably predictable events from the motion data (transition points) were used to segment each trial into phases. The first phase, the \"access phase,\" consisted of identifying the internal jugular vein (IJV) and carotid artery and securing venous access. The first transition point was the motion associated with placing the probe down after successfully gaining venous access. The second phase, known as the \"wire phase,\" involved threading the guidewire through the needle. In this phase, only needle hand motion was considered as the probe was stationary. The second transition point was the motion associated with picking up the probe again for the final phase, the \"confirmation phase\", which consisted of confirming the intravascular position of the wire with the ultrasound in both the transverse and longitudinal planes; the trial concluded when the dilator touched the 'skin' of the manikin. Segmentation of the trials was achieved with the aid of path length-time graphs, an example of which is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMotion and Statistical Analysis\u003c/h2\u003e \u003cp\u003eMotion analysis was performed using an in-house algorithm developed in R 4.1.2 (R Foundation for Statistical Computing, Vienna, Austria). Motion metrics for the needle hand and probe were summed for the analysis. Normality of the path length, rotational sum, translational movements, rotational movements, and time data was assessed via the Shapiro-Wilk Test and found to be non-normally distributed. Therefore, Mann-Whitney \u003cem\u003eU\u003c/em\u003e tests were used to compare the motion metrics between experts and trainees. Motion metrics are reported as median\u0026thinsp;\u0026plusmn;\u0026thinsp;interquartile range. A significance threshold of 0.05 was set, and the false discovery rate was controlled using the Benjamini-Hochberg correction. The ability of each phase to predict an expert operator was evaluated using the receiver operating characteristic (ROC) curve analysis. The Bland-Altman method was used to compare the observed time-to-event on the video to the predicted time-to-event from the motion data.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eMotion data were recorded for 41 trials from experts and 83 trials from trainees with a mean number of 4 trials per participant. All trials where participants did not fully execute the standardized steps (n\u0026thinsp;=\u0026thinsp;11) were excluded from the analysis. Therefore, all 41 trials for experts and 72 trials from trainees were included in the analysis.\u003c/p\u003e \u003cp\u003eExperts had significantly lower motion metrics (where lower is better) than trainees for the complete trial (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Bland-Altman plot showed comparable measurements of time-to-event from motion data and video recordings (mean difference\u0026thinsp;=\u0026thinsp;0.08s; 95% confidence interval = (-1.55s) \u0026ndash; (+\u0026thinsp;1.72s)) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). When the data was analyzed by phases, significant differences were still observed between the cohorts for all metrics in the first two phases (except path length in the access phase) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These findings were supported by the area-under-the-curve (AUC) from the ROC analysis and the fit of the models (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which was similar for the complete trial and the first two phases (AUC: complete trial\u0026thinsp;=\u0026thinsp;0.94; access phase\u0026thinsp;=\u0026thinsp;0.96; wire phase\u0026thinsp;=\u0026thinsp;0.95). Conversely, the performance of the confirmation phase showed relatively less disparity between the two cohorts, as evidenced by the motion metrics and the model parameters results.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eA comparison of motion metrics between trainees and experts across different phases of central venous access\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotion metric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrainees\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;72 trials)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExperts\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;41 trials)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eFull Trial\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1102.7\u0026thinsp;\u0026plusmn;\u0026thinsp;320.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e863.2\u0026thinsp;\u0026plusmn;\u0026thinsp;322.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRotational Sum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5055\u0026thinsp;\u0026plusmn;\u0026thinsp;3298.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2936\u0026thinsp;\u0026plusmn;\u0026thinsp;1886.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTranslational movements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119.5\u0026thinsp;\u0026plusmn;\u0026thinsp;41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81\u0026thinsp;\u0026plusmn;\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRotational Movements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e245\u0026thinsp;\u0026plusmn;\u0026thinsp;100.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141\u0026thinsp;\u0026plusmn;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.7\u0026thinsp;\u0026plusmn;\u0026thinsp;53.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.2\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAccess Phase\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e222.9\u0026thinsp;\u0026plusmn;\u0026thinsp;102.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200.8\u0026thinsp;\u0026plusmn;\u0026thinsp;107.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRotational Sum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1639.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e678.4\u0026thinsp;\u0026plusmn;\u0026thinsp;958.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTranslational movements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.5\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRotational Movements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.5\u0026thinsp;\u0026plusmn;\u0026thinsp;52.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.0\u0026thinsp;\u0026plusmn;\u0026thinsp;26.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.7\u0026thinsp;\u0026plusmn;\u0026thinsp;14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWire Phase (needle hand only)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e374.2\u0026thinsp;\u0026plusmn;\u0026thinsp;123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e245.5\u0026thinsp;\u0026plusmn;\u0026thinsp;136.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRotational Sum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1290.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1053.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e655.7\u0026thinsp;\u0026plusmn;\u0026thinsp;198.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTranslational movements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRotational Movements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.5\u0026thinsp;\u0026plusmn;\u0026thinsp;31.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.8\u0026thinsp;\u0026plusmn;\u0026thinsp;16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 .0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConfirmation Phase\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e311.2\u0026thinsp;\u0026plusmn;\u0026thinsp;177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e366.1\u0026thinsp;\u0026plusmn;\u0026thinsp;160.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRotational Sum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2002.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1276.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1243.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1005.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTranslational movements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.5\u0026thinsp;\u0026plusmn;\u0026thinsp;18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.0\u0026thinsp;\u0026plusmn;\u0026thinsp;18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRotational Movements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.5\u0026thinsp;\u0026plusmn;\u0026thinsp;35.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.0\u0026thinsp;\u0026plusmn;\u0026thinsp;26.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e*Statistically significant result; p values are corrected by Benjamini-Hochberg Method\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWhile all three phases could be used to distinguish between novice and expert operators, the ROC analysis showed that only the access phase or wire phase can be effectively used as a surrogate for the performance of the entire procedure. This may be because these phases require the most dexterity and coordination, one involving simultaneous ultrasound and needle coordination and the other requiring general dexterity with wire manipulation. Given that the access phase is performed first, it is likely the easiest to evaluate on a live patient and was the most valuable single segment in this simulation study. Future evaluation of the ability to use the access phase to differentiate novices from experts in the clinical setting is needed to validate this finding.\u003c/p\u003e \u003cp\u003eDeconstructing tasks and analyzing key segments of procedures to assist in teaching have been investigated for open surgical [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], laparoscopic [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and ultrasound-guided tasks [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This technique, in combination with motion metrics, can provide individualized feedback to trainees and potentially allow them to train components selectively. In the current study, gaining venous access was a key step that differentiated experts from non-experts, indicating that practice directed towards mastering this phase would be of the highest yield in training. This is supported by earlier studies examining the learning curve of ultrasound-guided central venous line placement [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Similarly, identifying segments that are not accurate indications of performance may consolidate data analysis.\u003c/p\u003e \u003cp\u003eUsing a segment that is least affected by factors not relevant to the procedure also helps reduce the effect on hand motion analysis from differences in setup. For example, if only the wire phase (which involves threading the wire through the needle) is used to assess technical skill, there is no need for a fixed starting position for the operator\u0026rsquo;s hands because the analysis starts only when access has been secured. For the access phase, although starting conditions may need to be standardized, the moment access has been secured, the probe then does not have to be placed in a standardized location.\u003c/p\u003e \u003cp\u003eThe ability of graphical data to be used to segment a procedure, and its high degree of correlation with video data, represents an advancement toward the practical incorporation of hand motion analysis in multistep procedures. Placing the ultrasound probe down or picking it back up produced a consistent motion signature that could be used to define the beginning or end of a phase. Similar motion signatures are likely to be found in other interventional procedures and may obviate the need for video recording for segmenting trials, which takes more time and increases the risk of breach of confidentiality for both trainees and (if done in the clinical setting) patients. Additionally, this may allow for computer-based automation of the process, further easing the transition into clinical use.\u003c/p\u003e \u003cp\u003eThis study has several limitations. The small sample size is a potential limitation; however, it did not limit the ability to detect differences between less and more experienced operators. The simulated nature of the procedure also represents a highly controlled environment with a set starting position for all the equipment, which may artificially reduce the variability of the motion recordings. A simulated environment lacks the fidelity of a live patient procedure where other elements, such as patient motion, may alter the results. Naturally, the segmentation of this central venous access simulation and the highest-yield segment for analysis cannot be directly translated to other procedures and would need to be revalidated in other procedures.\u003c/p\u003e \u003cp\u003eThis study demonstrates the proof-of-concept of using hand motion analysis to not only compare operators but to reliably segment a procedure and identify a segment that could accurately represent the operator\u0026rsquo;s overall performance. This could eliminate the future need for video recording trials for evaluation and eliminate the need for recording motion data for all the steps in a simulated central venous access procedure. Applying these concepts to even longer, more complex procedures are warranted to extend the utility of hand motion analysis into real-world practice.\u003c/p\u003e"},{"header":" Statements and Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was presented as an abstract at the Society of Interventional Radiology Annual Meeting 2023. The traditional poster received the Best Original Scientific Research Poster Award.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Information:\u0026nbsp;\u003c/strong\u003eThis work was supported by CRICO\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval: \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board (IRB) at Beth Israel Deaconess Medical Center\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVerbal consent was obtained from all participants.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eV. Datta, A. Chang, S. Mackay, A. Darzi, The relationship between motion analysis and surgical technical assessments., American Journal of Surgery 184 (2002) 70\u0026ndash;73. https://doi.org/10.1016/s0002-9610(02)00891-7.\u003c/li\u003e\n \u003cli\u003eM.A. Hayter, Z. Friedman, M.D. Bould, J.G. Hanlon, R. Katznelson, B. Borges, V.N. Naik, Validation of the Imperial College Surgical Assessment Device (ICSAD) for labour epidural placement, Can J Anesth/J Can Anesth 56 (2009) 419\u0026ndash;426. https://doi.org/10.1007/s12630-009-9090-1.\u003c/li\u003e\n \u003cli\u003eK.J. Chin, C. Tse, V. Chan, J.S. Tan, C.M. Lupu, M. Hayter, Hand motion analysis using the imperial college surgical assessment device: validation of a novel and objective performance measure in ultrasound-guided peripheral nerve blockade., Regional Anesthesia and Pain Medicine 36 (2011) 213\u0026ndash;219. https://doi.org/10.1097/AAP.0b013e31820d4305.\u003c/li\u003e\n \u003cli\u003eR. Matyal, J.D. Mitchell, P.E. Hess, B. Chaudary, R. Bose, J.S. Jainandunsing, V. Wong, F. Mahmood, Simulator-based Transesophageal Echocardiographic Training with Motion Analysis, Anesthesiology 121 (2014) 389\u0026ndash;399. https://doi.org/10.1097/ALN.0000000000000234.\u003c/li\u003e\n \u003cli\u003eR. Matyal, F. Mahmood, Z.O. Knio, S.B. Jones, L. Yeh, R. Amir, R. Bose, J.D. Mitchell, Evaluation of the quality of transesophageal echocardiography images and verification of proficiency, Echo Research and Practice 5 (2018) 89\u0026ndash;95. https://doi.org/10.1530/ERP-18-0002.\u003c/li\u003e\n \u003cli\u003eJ. Varas, P. Achurra, F. Le\u0026oacute;n, R. Castillo, N. De La Fuente, R. Aggarwal, L. Clede, M.P. Bravo, M. Corvetto, R. Monta\u0026ntilde;a, Assessment of central venous catheterization in a simulated model using a motion-tracking device: an experimental validation study., Annals of Surgical Innovation and Research 10 (2016) 2. https://doi.org/10.1186/s13022-016-0025-6.\u003c/li\u003e\n \u003cli\u003eD. Clinkard, M. Holden, T. Ungi, D. Messenger, C. Davison, G. Fichtinger, R. McGraw, The Development and Validation of Hand Motion Analysis to Evaluate Competency in Central Line Catheterization, Acad Emerg Med 22 (2015) 212\u0026ndash;218. https://doi.org/10.1111/acem.12590.\u003c/li\u003e\n \u003cli\u003eJ.L. Weinstein, F. El-Gabalawy, A. Sarwar, S.S. DeBacker, S. Faintuch, S.J. Berkowitz, J.C. Bulman, M.R. Palmer, R. Matyal, F. Mahmood, M. Ahmed, Analysis of Kinematic Differences in Hand Motion between Novice and Experienced Operators in IR: A Pilot Study, Journal of Vascular and Interventional Radiology 32 (2021) 226\u0026ndash;234. https://doi.org/10.1016/j.jvir.2020.10.010.\u003c/li\u003e\n \u003cli\u003eM.A. Corvetto, F.R. Altermatt, Tracking Motion Devices as Assessment Tools in Anesthesia Procedures: Have We Been Using Them Well?, CJEM 19 (2017) 412\u0026ndash;413. https://doi.org/10.1017/cem.2017.337.\u003c/li\u003e\n \u003cli\u003eV. Baribeau, A. Sharkey, K.R. Murugappan, D.P. Walsh, V.T. Wong, A. Bose, O. Chaudhary, J. Weinstein, R. Matyal, F. Mahmood, J.D. Mitchell, Assessing Skill Acquisition in Anesthesiology Interns Practicing Central Venous Catheter Placement through Advancements in Motion Analysis, Journal of Cardiothoracic and Vascular Anesthesia (2022). https://doi.org/10.1053/j.jvca.2022.01.039.\u003c/li\u003e\n \u003cli\u003eA. Dosis, R. Aggarwal, F. Bello, K. Moorthy, Y. Munz, D. Gillies, A. Darzi, Synchronized video and motion analysis for the assessment of procedures in the operating theater., Archives of Surgery 140 (2005) 293\u0026ndash;299. https://doi.org/10.1001/archsurg.140.3.293.\u003c/li\u003e\n \u003cli\u003eS.S. Vedula, A. Malpani, N. Ahmidi, S. Khudanpur, G. Hager, C.C.G. Chen, Task-Level vs. Segment-Level Quantitative Metrics for Surgical Skill Assessment, Journal of Surgical Education 73 (2016) 482\u0026ndash;489. https://doi.org/10.1016/j.jsurg.2015.11.009.\u003c/li\u003e\n \u003cli\u003eV. Baribeau, J. Weinstein, V.T. Wong, A. Sharkey, D.N. Lodico, R. Matyal, F. Mahmood, J.D. Mitchell, Motion-Tracking Machines and Sensors: Advancing Education Technology, Journal of Cardiothoracic and Vascular Anesthesia 36 (2022) 303\u0026ndash;308. https://doi.org/10.1053/j.jvca.2021.07.036.\u003c/li\u003e\n \u003cli\u003eJ.L. Weinstein, H. Ali, A. Sarwar, J.R. Dadour, O.R. Brook, J.D. Mitchell, R. Matyal, M.R. Palmer, C. MacLellan, M. Ahmed, Evaluation of Technical Performance of Ultrasound-Guided Procedures through Hand Motion Analysis: an Exploration of Motion Metrics, Journal of Vascular and Interventional Radiology (2023). https://doi.org/10.1016/j.jvir.2023.05.015.\u003c/li\u003e\n \u003cli\u003eO. Metrouh, H. Ali, S.E.S. DeBacker, C.J. McCarthy, C. MacLellan, M.R. Palmer, M. Ahmed, J.L. Weinstein, The Effect of Time Pressure on Motion Economy and Smoothness of Interventional Radiology Trainee Performance in Simulated Central Venous Line Placement, Cardiovasc Intervent Radiol (2024). https://doi.org/10.1007/s00270-024-03831-9.\u003c/li\u003e\n \u003cli\u003eT. Nazari, K. Bogomolova, M. Ridderbos, M.E.W. Dankbaar, J.J.G. van Merri\u0026euml;nboer, J.F. Lange, T. Wiggers, J.A. van der Hage, Global versus task-specific postoperative feedback in surgical procedure learning, Surgery 170 (2021) 81\u0026ndash;87. https://doi.org/10.1016/j.surg.2020.12.038.\u003c/li\u003e\n \u003cli\u003eM.A. Farcas, M.O. Trudeau, A. Nasr, J.T. Gerstle, B. Carrillo, G. Azzie, Analysis of motion in laparoscopy: the deconstruction of an intra-corporeal suturing task., Surgical Endoscopy 31 (2017) 3130\u0026ndash;3139. https://doi.org/10.1007/s00464-016-5337-4.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-medical-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Medical Systems](https://www.springer.com/journal/10916)","snPcode":"10916","submissionUrl":"https://submission.nature.com/new-submission/10916/3","title":"Journal of Medical Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Hand motion analysis, procedure segmentation, technical performance, simulation","lastPublishedDoi":"10.21203/rs.3.rs-5054500/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5054500/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aims to examine if the hand motions of operators associated with certain parts of central venous access are more important than others in distinguishing between experts and non-experts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExperts (n = 10) and Trainees (PGY2; n = 18) performed central venous access on a phantom 4 times each as their needle hand and ultrasound probe motions were tracked. Path length-time graphs were used to divide the procedure into three phases: (1) the access phase: visualizing the internal jugular vein on ultrasound and needle placement; (2) the wire phase: passing a wire through the needle; and (3) the confirmation phase: confirming the intravascular wire position and threading a dilator on the wire. Comparisons between trainees and experts were made for the complete trial, and each phase using Mann-Whitney \u003cem\u003eU\u003c/em\u003e tests with Benjamini-Hochberg correction. Receiver Operating Characteristic analysis was performed to compare the performance of each phase in differentiating between experts and trainees.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMotion data from 10 experts and 18 trainees was analyzed. Experts and trainees differed significantly for all the motion metrics (p\u0026lt;0.001). A comparison of the phases showed that the access phase (AUC = 0.96; R2 = 0.79) and the wire phase (AUC = 0.95; R2 = 0.59) were able to distinguish between experts and trainees with an accuracy comparable to the complete trial (AUC = 0.94; R2 = 0.69).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe access phase of simulated central venous access can best differentiate between experts and trainees. This sample of hand motion performance may be able to simplify motion analysis of technical performance and obviate the need for recording hand motion for the entire procedure.\u003c/p\u003e","manuscriptTitle":"Hand Motion Analysis of Different Segments of a Procedure: Is One Segment Enough?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-27 06:38:07","doi":"10.21203/rs.3.rs-5054500/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-01T20:16:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-01T18:52:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68207574221111481480724225532179882102","date":"2025-03-21T15:59:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135995759996892359597095670233295839395","date":"2025-01-13T17:28:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-25T04:30:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48166008316321632422254888125625830591","date":"2024-09-19T07:41:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180231065413667942347307641393861734286","date":"2024-09-19T04:09:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-19T02:41:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-19T02:39:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-12T11:55:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Medical Systems","date":"2024-09-09T01:16:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-medical-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Medical Systems](https://www.springer.com/journal/10916)","snPcode":"10916","submissionUrl":"https://submission.nature.com/new-submission/10916/3","title":"Journal of Medical Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ac32e65b-ee2a-4344-b585-4a26c7f0c172","owner":[],"postedDate":"May 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-05-27T06:38:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-27 06:38:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5054500","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5054500","identity":"rs-5054500","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}